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WorkResearchLuciding

Research / 2015–2019

Luciding

An EEG wearable, sleep-data pipeline, and consumer neurotechnology stack for lucid-dream research.

Luciding built LucidCatcher: a connected headset designed to detect REM sleep and time audio and light stimulation.

Luciding product page explaining the LucidCatcher sleep wearable
The product story translated a complex sleep-sensing loop into a consumer experience.
Role
Co-founder and CTO
Timeframe
2015–2019
System
Research
  • EEG hardware
  • firmware
  • edge ML
  • mobile
  • sleep research
  • neurotechnology
01

Overview

The system in context.

Founded in San Francisco in 2015, Luciding paired custom EEG hardware with mobile sleep tracking, guided experiences, and a backend learning loop. The LucidCatcher concept used sleep-state detection to time sensory cues intended to help a sleeper recognize a dream.

As co-founder and CTO, Nikita worked across firmware, on-device signal processing, mobile product, cloud data, research operations, and global manufacturing sourcing. The company and its lucid-dream research were covered by The Atlantic and IEEE Spectrum.

What shipped

  • Built a connected EEG headset with accelerometer sensing and real-time data streaming.
  • Developed the signal path spanning firmware, edge processing, mobile sessions, and backend learning.
  • Ran a multi-year research program and a public Kickstarter campaign.
  • Earned independent coverage from The Atlantic and IEEE Spectrum.
02

Measured context

The numbers, with their meaning intact.

EEG channels
6

Founder-reported LucidCatcher hardware specification in the preserved portfolio.

nights of sleep data
1K+

Portfolio-reported dataset used in the product's backend improvement loop.

research trials
3 yrs

The preserved project account describes three years of trials and product research.

03

Product record

Screenshots from the product and project record. Open any frame for a closer view.

01The product story translated a complex sleep-sensing loop into a consumer experience.
02Guided content and session playback connected the hardware to the dream experience.
04

Build story

From signal to shipped system.

  1. 01 / Sense

    Read the sleeping body

    EEG and motion data formed the input for detecting sleep state on a constrained wearable device.

  2. 02 / Time

    Deliver a cue at the right moment

    Signal processing connected REM-state estimates to carefully timed audio and light stimulation.

  3. 03 / Learn

    Turn nights into a feedback loop

    Mobile sessions and backend aggregation created a path for improving models across a growing sleep dataset.

Project links

Open the live product, working demo, publication, or repository.

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